The Stanford University study analyzed 93 million traffic stops from 21 state patrol agencies and 29 municipal police departments between the years of 2001 and 2017.

Researchers then analyzed the traffic-stop data in relation to the number of people of driving age within each jurisdiction and controlled for demographics, gender, reasons for traffic stops and other factors to try to create the most standardized set of data possible.

The results, which reflect experiences that have long been shared by people of color, revealed an observable racial bias in both traffic stops and subsequent decisions to conduct vehicle searches.

“Relative to their share of the residential population, we find that black drivers are, on average, stopped more often than whites,” reads the study, released by the Stanford Computational Policy Lab and featuring data organized by the Stanford Open Policing Project.

The study’s authors acknowledged that basing this disparity on bias is hard to do in a statistically significant way, so they also analyzed the data using what they called the “veil of darkness” test. Essentially, they looked at the racial breakdown of only the traffic stops made after dark, when the race of a motorist is harder to discern.

Even when applied to different subsets of data, the results “[showed] a marked drop in the proportion of drivers stopped after dusk who are black, suggestive of discrimination in stop decisions.”

The study also looked at data related to police searches of stopped cars, and found searches on black and Hispanic motorists seemed to have a “lower bar” than searches on white motorists.

CNN has reached out to the National Association of Chiefs of Police for comment on the study’s findings but has not heard back.

The data goes beyond issues of black and white

On its surface, the results lend quantifiable significance to what has long been said by activists and ethnic minorities in America: Motorists of color are often subjected to disproportionate levels of traffic stops and police searches.

However, that isn’t the only purpose of the study, or the data that fueled it.

“Our work doesn’t necessarily reveal anything new; activists and individuals of color have long presented anecdotal evidence of this kind of bias,” Stanford data scientist Amy Shoemaker, who worked on the project, tells CNN. “The new part is being able to understand it in quantifiable terms.”

This kind of standardization can help inform policy change on state and municipal levels.

“In addition to the national picture, what we are also offering is clean public data to journalists, analysts and policy makers so they can use local context for their policies,” Shoemaker says.

“A lot of policy makers feel the need to have data-driven decisions, and so this is a data-driven approach to racial profiling,”

Although the national picture the data presents makes for a captivating headline, Shoemaker says the research is especially valuable on a local and municipal level, where individual departments and policy makers can use it to spot trends specific to their area and make finely tuned changes.

“It’s good to have a general understanding, but each place has its caveats, and each jurisdiction has its own limitations or ways of doing things,” Shoemaker says.

For instance, in Nashville, she says, the data for the area was so specific that a local police department was able to see that there was a preponderance of stops in mostly poor, black areas for things like taillights and license plates.

“The department (there) was understaffed, and when they realized those stops don’t actually reduce crime at all, they were much more amenable to redirecting their resources,” Shoemaker says.

But there’s no standardized way of reporting traffic stops

Shoemaker says one of the researchers’ biggest challenges in doing the study was making sure the data they got was standardized in a way that made sense, so that, for instance, one state or jurisdiction’s definition of race or type of traffic violation wasn’t different than another.

“There were different ways of recording race by department,” she says. “Michigan for example, recorded unknown race for 50% of the stops, which isn’t usable data for us.”

Another hurdle was the fact that states and municipalities don’t have one standardized way of collecting and publishing information about traffic stops. Some states, like Florida and Illinois, keep extensive records while other states have very few requirements for law enforcement to document and record their stops.

Categorizing the reasons for searches after stops also proved difficult.

Shoemaker and the other researchers involved in the project believe more standardized record keeping by police could help clarify the overall picture of racial bias and offer solutions in the future.

“We hope that police departments start regularly analyzing their data and report the results of their findings,” they say in the study.

“Such analyses might include estimates of stop, search, and hit rates, stratified by race, age, gender, and location; distribution of stop reasons by race; and trends over time. More ambitiously, departments could use their data to design statistically informed guidelines that encourage more consistent, efficient, and equitable decisions.”